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      "source": [
        "from torchvision import datasets\n",
        "import torch\n",
        "data_folder = '~/data/FMNIST' # This can be any directory you want to \n",
        "# download FMNIST to\n",
        "fmnist = datasets.FashionMNIST(data_folder, download=True, train=True)\n",
        "tr_images = fmnist.data\n",
        "tr_targets = fmnist.targets"
      ],
      "execution_count": null,
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          "output_type": "stream",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
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            "Extracting /root/data/FMNIST/FashionMNIST/raw/train-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
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          "text": [
            "Extracting /root/data/FMNIST/FashionMNIST/raw/train-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
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          "text": [
            "Extracting /root/data/FMNIST/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n",
            "\n",
            "\n"
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          "text": [
            "Extracting /root/data/FMNIST/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
            "Processing...\n",
            "Done!\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:469: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n",
            "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S4Ss3qAj6cCN"
      },
      "source": [
        "val_fmnist = datasets.FashionMNIST(data_folder, download=True, train=False)\n",
        "val_images = val_fmnist.data\n",
        "val_targets = val_fmnist.targets"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MhUgyxQv6dWF"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kWEHrvHpxC6Z"
      },
      "source": [
        "### High Learning Rate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wHgNxifc6edk"
      },
      "source": [
        "class FMNISTDataset(Dataset):\n",
        "    def __init__(self, x, y):\n",
        "        x = x.float()/255\n",
        "        x = x.view(-1,28*28)\n",
        "        self.x, self.y = x, y \n",
        "    def __getitem__(self, ix):\n",
        "        x, y = self.x[ix], self.y[ix] \n",
        "        return x.to(device), y.to(device)\n",
        "    def __len__(self): \n",
        "        return len(self.x)\n",
        "\n",
        "from torch.optim import SGD, Adam\n",
        "def get_model():\n",
        "    model = nn.Sequential(\n",
        "        nn.Linear(28 * 28, 1000),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(1000, 10)\n",
        "    ).to(device)\n",
        "\n",
        "    loss_fn = nn.CrossEntropyLoss()\n",
        "    optimizer = Adam(model.parameters(), lr=1e-1)\n",
        "    return model, loss_fn, optimizer\n",
        "\n",
        "def train_batch(x, y, model, opt, loss_fn):\n",
        "    model.train()\n",
        "    prediction = model(x)\n",
        "    batch_loss = loss_fn(prediction, y)\n",
        "    batch_loss.backward()\n",
        "    optimizer.step()\n",
        "    optimizer.zero_grad()\n",
        "    return batch_loss.item()\n",
        "\n",
        "def accuracy(x, y, model):\n",
        "    model.eval()\n",
        "    # this is the same as @torch.no_grad \n",
        "    # at the top of function, only difference\n",
        "    # being, grad is not computed in the with scope\n",
        "    with torch.no_grad():\n",
        "        prediction = model(x)\n",
        "    max_values, argmaxes = prediction.max(-1)\n",
        "    is_correct = argmaxes == y\n",
        "    return is_correct.cpu().numpy().tolist()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CfnVtUMO6nhR"
      },
      "source": [
        "def get_data(): \n",
        "    train = FMNISTDataset(tr_images, tr_targets) \n",
        "    trn_dl = DataLoader(train, batch_size=32, shuffle=True)\n",
        "    val = FMNISTDataset(val_images, val_targets) \n",
        "    val_dl = DataLoader(val, batch_size=len(val_images), shuffle=False)\n",
        "    return trn_dl, val_dl"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wAN-GtKb6o83"
      },
      "source": [
        "@torch.no_grad()\n",
        "def val_loss(x, y, model):\n",
        "    prediction = model(x)\n",
        "    val_loss = loss_fn(prediction, y)\n",
        "    return val_loss.item()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7EhlA61S6qM3"
      },
      "source": [
        "trn_dl, val_dl = get_data()\n",
        "model, loss_fn, optimizer = get_model()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h-yph5GO6rQ6",
        "outputId": "96d18492-ffaa-4db1-f86b-fabbabb9d5ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "train_losses, train_accuracies = [], []\n",
        "val_losses, val_accuracies = [], []\n",
        "for epoch in range(5):\n",
        "    print(epoch)\n",
        "    train_epoch_losses, train_epoch_accuracies = [], []\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        batch_loss = train_batch(x, y, model, optimizer, loss_fn)\n",
        "        train_epoch_losses.append(batch_loss) \n",
        "    train_epoch_loss = np.array(train_epoch_losses).mean()\n",
        "\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        is_correct = accuracy(x, y, model)\n",
        "        train_epoch_accuracies.extend(is_correct)\n",
        "    train_epoch_accuracy = np.mean(train_epoch_accuracies)\n",
        "    for ix, batch in enumerate(iter(val_dl)):\n",
        "        x, y = batch\n",
        "        val_is_correct = accuracy(x, y, model)\n",
        "        validation_loss = val_loss(x, y, model)\n",
        "    val_epoch_accuracy = np.mean(val_is_correct)\n",
        "    train_losses.append(train_epoch_loss)\n",
        "    train_accuracies.append(train_epoch_accuracy)\n",
        "    val_losses.append(validation_loss)\n",
        "    val_accuracies.append(val_epoch_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HtZsoP8w6sNY",
        "outputId": "2fc9ff64-e257-403f-ca81-4b619d10eda4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "source": [
        "epochs = np.arange(5)+1\n",
        "import matplotlib.ticker as mtick\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as mticker\n",
        "%matplotlib inline\n",
        "plt.subplot(211)\n",
        "plt.plot(epochs, train_losses, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_losses, 'r', label='Validation loss')\n",
        "plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation loss with 0.1 learning rate')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()\n",
        "plt.subplot(212)\n",
        "plt.plot(epochs, train_accuracies, 'bo', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracies, 'r', label='Validation accuracy')\n",
        "plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation accuracy with 0.1 learning rate')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.gca().set_yticklabels(['{:.0f}%'.format(x*100) for x in plt.gca().get_yticks()]) \n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oe7U2lsn8Cub",
        "outputId": "74d286e1-f7ac-4df8-8881-365625253b19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for ix, par in enumerate(model.parameters()):\n",
        "    if(ix==0):\n",
        "        plt.hist(par.cpu().detach().numpy().flatten())\n",
        "        plt.title('Distribution of weights conencting input to hidden layer')\n",
        "        plt.show()\n",
        "    elif(ix ==1):\n",
        "        plt.hist(par.cpu().detach().numpy().flatten())\n",
        "        plt.title('Distribution of biases of hidden layer')\n",
        "        plt.show()\n",
        "    elif(ix==2):\n",
        "        plt.hist(par.cpu().detach().numpy().flatten())\n",
        "        plt.title('Distribution of weights conencting hidden to output layer')\n",
        "        plt.show()\n",
        "    elif(ix ==3):\n",
        "        plt.hist(par.cpu().detach().numpy().flatten())\n",
        "        plt.title('Distribution of biases of output layer')\n",
        "        plt.show() "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EFAUGptW7By6"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qFosnD377CVI"
      },
      "source": [
        "### Medium learning rate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qixgqA-x7Dzv"
      },
      "source": [
        "def get_model():\n",
        "    model = nn.Sequential(\n",
        "        nn.Linear(28 * 28, 1000),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(1000, 10)\n",
        "    ).to(device)\n",
        "\n",
        "    loss_fn = nn.CrossEntropyLoss()\n",
        "    optimizer = Adam(model.parameters(), lr=1e-3)\n",
        "    return model, loss_fn, optimizer"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7RJpjJAc7G1Y"
      },
      "source": [
        "trn_dl, val_dl = get_data()\n",
        "model, loss_fn, optimizer = get_model()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AXEL4biQ7KGE",
        "outputId": "942c8acd-eca8-42a8-8121-5dbf3aef6b99",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "train_losses, train_accuracies = [], []\n",
        "val_losses, val_accuracies = [], []\n",
        "for epoch in range(5):\n",
        "    print(epoch)\n",
        "    train_epoch_losses, train_epoch_accuracies = [], []\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        batch_loss = train_batch(x, y, model, optimizer, loss_fn)\n",
        "        train_epoch_losses.append(batch_loss) \n",
        "    train_epoch_loss = np.array(train_epoch_losses).mean()\n",
        "\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        is_correct = accuracy(x, y, model)\n",
        "        train_epoch_accuracies.extend(is_correct)\n",
        "    train_epoch_accuracy = np.mean(train_epoch_accuracies)\n",
        "    for ix, batch in enumerate(iter(val_dl)):\n",
        "        x, y = batch\n",
        "        val_is_correct = accuracy(x, y, model)\n",
        "        validation_loss = val_loss(x, y, model)\n",
        "    val_epoch_accuracy = np.mean(val_is_correct)\n",
        "    train_losses.append(train_epoch_loss)\n",
        "    train_accuracies.append(train_epoch_accuracy)\n",
        "    val_losses.append(validation_loss)\n",
        "    val_accuracies.append(val_epoch_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lg-Po1mj7MD6",
        "outputId": "99e3efca-568e-4e98-b8b6-0440815398b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "source": [
        "epochs = np.arange(5)+1\n",
        "import matplotlib.ticker as mtick\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as mticker\n",
        "%matplotlib inline\n",
        "plt.subplot(211)\n",
        "plt.plot(epochs, train_losses, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_losses, 'r', label='Validation loss')\n",
        "plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation loss with 0.001 learning rate')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()\n",
        "plt.subplot(212)\n",
        "plt.plot(epochs, train_accuracies, 'bo', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracies, 'r', label='Validation accuracy')\n",
        "plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation accuracy with 0.001 learning rate')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.gca().set_yticklabels(['{:.0f}%'.format(x*100) for x in plt.gca().get_yticks()]) \n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CgmXa6F67d-C",
        "outputId": "9e9beb25-1807-4efd-8789-fc08b25fd98d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for ix, par in enumerate(model.parameters()):\n",
        "    if(ix==0):\n",
        "        plt.hist(par.cpu().detach().numpy().flatten())\n",
        "        plt.title('Distribution of weights conencting input to hidden layer')\n",
        "        plt.show()\n",
        "    elif(ix ==1):\n",
        "        plt.hist(par.cpu().detach().numpy().flatten())\n",
        "        plt.title('Distribution of biases of hidden layer')\n",
        "        plt.show()\n",
        "    elif(ix==2):\n",
        "        plt.hist(par.cpu().detach().numpy().flatten())\n",
        "        plt.title('Distribution of weights conencting hidden to output layer')\n",
        "        plt.show()\n",
        "    elif(ix ==3):\n",
        "        plt.hist(par.cpu().detach().numpy().flatten())\n",
        "        plt.title('Distribution of biases of output layer')\n",
        "        plt.show() "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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nAjOzinMiMDOrOCcCM7OKcyIwM6s4JwIzs4pzIjAzqzgnAjOzimspEUgaK+kSSb+TdJekV0oaL2mZpBX5PC7rStKZknok3SZp/1I787L+CknzBmunzMysda2eEXwF+PeI2Ad4KXAXcAJwVURMB67KeYDDgOn5WACcBSBpPLAQOBA4AFhYSx5mZtY9/SYCSTsDrwHOAYiIv0bEQ8AcYHFWWwwcldNzgPOicAMwVtIk4FBgWURsiogHgWXA7I7ujZmZta2VM4I9gV7g25J+Lelbkp4LTIyItVlnHTAxpycDq0rrr86yZuVbkLRA0nJJy3t7e9vbGzMza1sriWA0sD9wVkS8DPgzzwwDARARAUQnAoqIsyNiZkTMnDBhQieaNDOzPrSSCFYDqyPixpy/hCIxrM8hH/J5Qy5fA0wtrT8ly5qVm5lZF/WbCCJiHbBK0guz6BDgTmApULvzZx5wWU4vBY7Ju4cOAjbnENKVwCxJ4/Ii8awsMzOzLhrdYr0PARdIGgPcCxxHkUSWSJoPrASOzrpXAIcDPcCjWZeI2CTpFODmrHdyRGzqyF6Ymdk2aykRRMStwMwGiw5pUDeA45u0swhY1E6AZmY2uPzLYjOzinMiMDOrOCcCM7OKcyIwM6s4JwIzs4pzIjAzqzgnAjOzinMiMDOrOCcCM7OKcyIwM6s4JwIzs4pzIjAzqzgnAjOzinMiMDOrOCcCM7OKcyIwM6s4JwIzs4pzIjAzqzgnAjOzinMiMDOruJYSgaT7JP1W0q2SlmfZeEnLJK3I53FZLklnSuqRdJuk/UvtzMv6KyTNG5xdMjOzdrRzRvD6iNgvImbm/AnAVRExHbgq5wEOA6bnYwFwFhSJA1gIHAgcACysJQ8zM+uegQwNzQEW5/Ri4KhS+XlRuAEYK2kScCiwLCI2RcSDwDJg9gC2b2ZmHdBqIgjgp5JukbQgyyZGxNqcXgdMzOnJwKrSuquzrFn5FiQtkLRc0vLe3t4WwzMzs201usV6r46INZL+Dlgm6XflhRERkqITAUXE2cDZADNnzuxIm2Zm1lxLZwQRsSafNwCXUozxr88hH/J5Q1ZfA0wtrT4ly5qVm5lZF/WbCCQ9V9JOtWlgFnA7sBSo3fkzD7gsp5cCx+TdQwcBm3MI6UpglqRxeZF4VpaZmVkXtTI0NBG4VFKt/ncj4t8l3QwskTQfWAkcnfWvAA4HeoBHgeMAImKTpFOAm7PeyRGxqWN7YmZm26TfRBAR9wIvbVC+ETikQXkAxzdpaxGwqP0wzcxssPiXxWZmFedEYGZWcU4EZmYV50RgZlZxTgRmZhXnRGBmVnFOBGZmFedEYGZWcU4EZmYV50RgZlZxTgRmZhXnRGBmVnFOBGZmFedEYGZWcU4EZmYV50RgZlZxTgRmZhXnRGBmVnFOBGZmFddyIpA0StKvJf045/eUdKOkHkkXSxqT5dvnfE8un1Zq48Qsv1vSoZ3eGTMza187ZwQfBu4qzZ8BfCki9gYeBOZn+XzgwSz/UtZD0gxgLvAiYDbwNUmjBha+mZkNVEuJQNIU4AjgWzkv4GDgkqyyGDgqp+fkPLn8kKw/B7goIh6PiD8APcABndgJMzPbdq2eEXwZ+ATwVM7vAjwUEU/m/Gpgck5PBlYB5PLNWf/p8gbrPE3SAknLJS3v7e1tY1fMzGxb9JsIJL0J2BARtwxBPETE2RExMyJmTpgwYSg2aWZWaaNbqPMq4EhJhwM7AM8DvgKMlTQ6v/VPAdZk/TXAVGC1pNHAzsDGUnlNeR0zM+uSfs8IIuLEiJgSEdMoLvZeHRHvAK4B3prV5gGX5fTSnCeXXx0RkeVz866iPYHpwE0d2xMzM9smrZwRNPNJ4CJJpwK/Bs7J8nOA8yX1AJsokgcRcYekJcCdwJPA8RHxtwFs38zMOqCtRBAR1wLX5vS9NLjrJyIeA97WZP3TgNPaDdLMzAaPf1lsZlZxTgRmZhXnRGBmVnFOBGZmFedEYGZWcU4EZmYV50RgZlZxTgRmZhXnRGBmVnFOBGZmFedEYGZWcU4EZmYV50RgZlZxTgRmZhXnRGBmVnFOBGZmFedEYGZWcU4EZmYV50RgZlZx/SYCSTtIuknSbyTdIemzWb6npBsl9Ui6WNKYLN8+53ty+bRSWydm+d2SDh2snTIzs9a1ckbwOHBwRLwU2A+YLekg4AzgSxGxN/AgMD/rzwcezPIvZT0kzQDmAi8CZgNfkzSqkztjZmbt6zcRROGRnN0uHwEcDFyS5YuBo3J6Ts6Tyw+RpCy/KCIej4g/AD3AAR3ZCzMz22YtXSOQNErSrcAGYBlwD/BQRDyZVVYDk3N6MrAKIJdvBnYplzdYx8zMuqSlRBARf4uI/YApFN/i9xmsgCQtkLRc0vLe3t7B2oyZmaW27hqKiIeAa4BXAmMljc5FU4A1Ob0GmAqQy3cGNpbLG6xT3sbZETEzImZOmDChnfDMzGwbtHLX0ARJY3P62cAbgbsoEsJbs9o84LKcXprz5PKrIyKyfG7eVbQnMB24qVM7YmZm22Z0/1WYBCzOO3yeBSyJiB9LuhO4SNKpwK+Bc7L+OcD5knqATRR3ChERd0haAtwJPAkcHxF/6+zumJlZu/pNBBFxG/CyBuX30uCun4h4DHhbk7ZOA05rP0wzMxss/mWxmVnFORGYmVWcE4GZWcU5EZiZVZwTgZlZxTkRmJlVnBOBmVnFORGYmVWcE4GZWcU5EZiZVZwTgZlZxTkRmJlVnBOBmVnFORGYmVWcE4GZWcU5EZiZVZwTgZlZxTkRmJlVnBOBmVnFORGYmVVcv4lA0lRJ10i6U9Idkj6c5eMlLZO0Ip/HZbkknSmpR9JtkvYvtTUv66+QNG/wdsvMzFrVyhnBk8DHImIGcBBwvKQZwAnAVRExHbgq5wEOA6bnYwFwFhSJA1gIHAgcACysJQ8zM+uefhNBRKyNiF/l9MPAXcBkYA6wOKstBo7K6TnAeVG4ARgraRJwKLAsIjZFxIPAMmB2R/fGzMza1tY1AknTgJcBNwITI2JtLloHTMzpycCq0mqrs6xZef02FkhaLml5b29vO+GZmdk2aDkRSNoR+D7wkYj4U3lZRAQQnQgoIs6OiJkRMXPChAmdaNLMzPrQUiKQtB1FErggIn6QxetzyId83pDla4CppdWnZFmzcjMz66JW7hoScA5wV0R8sbRoKVC782cecFmp/Ji8e+ggYHMOIV0JzJI0Li8Sz8oyMzProtEt1HkV8C7gt5JuzbJPAacDSyTNB1YCR+eyK4DDgR7gUeA4gIjYJOkU4Oasd3JEbOrIXpiZ2TbrNxFExPWAmiw+pEH9AI5v0tYiYFE7AZqZ2eDyL4vNzCrOicDMrOKcCMzMKs6JwMys4pwIzMwqzonAzKzinAjMzCrOicDMrOKcCMzMKs6JwMys4pwIzMwqrpU/OmdmBsC0Ey7vynbvO/2Irmy3KnxGYGZWcU4EZmYV50RgZlZxTgRmZhXnRGBmVnFOBGZmFedEYGZWcf0mAkmLJG2QdHupbLykZZJW5PO4LJekMyX1SLpN0v6ldeZl/RWS5g3O7piZWbtaOSM4F5hdV3YCcFVETAeuynmAw4Dp+VgAnAVF4gAWAgcCBwALa8nDzMy6q99EEBHXAZvqiucAi3N6MXBUqfy8KNwAjJU0CTgUWBYRmyLiQWAZWycXMzPrgm39ExMTI2JtTq8DJub0ZGBVqd7qLGtWvhVJCyjOJth99923MTyzwdetP7dg1mkDvlgcEQFEB2KptXd2RMyMiJkTJkzoVLNmZtbEtiaC9TnkQz5vyPI1wNRSvSlZ1qzczMy6bFsTwVKgdufPPOCyUvkxeffQQcDmHEK6EpglaVxeJJ6VZWZm1mX9XiOQdCHwOmBXSasp7v45HVgiaT6wEjg6q18BHA70AI8CxwFExCZJpwA3Z72TI6L+ArSZmXVBv4kgIt7eZNEhDeoGcHyTdhYBi9qKzszMBp1/WWxmVnFOBGZmFedEYGZWcU4EZmYV50RgZlZxTgRmZhXnRGBmVnFOBGZmFedEYGZWcU4EZmYV50RgZlZxTgRmZhXnRGBmVnFOBGZmFedEYGZWcU4EZmYV50RgZlZxTgRmZhXX77+qNBvOpp1webdDMBvxfEZgZlZxQ54IJM2WdLekHkknDPX2zcxsS0M6NCRpFPBvwBuB1cDNkpZGxJ1DGYeZjSzdGgK87/QjurLdoTbU1wgOAHoi4l4ASRcBcwAnghHOY/VmI9dQJ4LJwKrS/GrgwHIFSQuABTn7iKS769rYFXhg0CLsrJEUK4yseEdSrDCy4h1JscIgxqszOt7kUPXtHu1UHnZ3DUXE2cDZzZZLWh4RM4cwpG02kmKFkRXvSIoVRla8IylWGFnxDtdYh/pi8Rpgaml+SpaZmVmXDHUiuBmYLmlPSWOAucDSIY7BzMxKhnRoKCKelPRB4EpgFLAoIu5os5mmw0bD0EiKFUZWvCMpVhhZ8Y6kWGFkxTssY1VEdDsGMzPrIv+y2Mys4pwIzMwqbtglAklvk3SHpKckNbzNStJUSddIujPrfri07CRJayTdmo/Dux1v1mv4pzXywvmNWX5xXkQfrFjHS1omaUU+j2tQ5/WlvrtV0mOSjspl50r6Q2nZfoMVa6vxZr2/lWJaWiofsr5tNV5J+0n6ZR4zt0n6H6Vlg96//f2JF0nbZ1/1ZN9NKy07McvvlnRop2Pbhlg/mp8Bt0m6StIepWUNj4kux3uspN5SXO8pLZuXx80KSfOGIt4tRMSwegD7Ai8ErgVmNqkzCdg/p3cCfg/MyPmTgI8Ps3hHAfcAewFjgN+U4l0CzM3prwMfGMRY/xU4IadPAM7op/54YBPwnJw/F3jrEPZtS/ECjzQpH7K+bTVe4O+B6Tm9G7AWGDsU/dvXcViq80/A13N6LnBxTs/I+tsDe2Y7o7oc6+tLx+YHarH2dUx0Od5jgf/XYN3xwL35PC6nxw1l/MPujCAi7oqI+l8T19dZGxG/yumHgbsofrU85FqJl9Kf1oiIvwIXAXMkCTgYuCTrLQaOGrxomZPbaHVbbwV+EhGPDmJMfWk33qd1oW+hhXgj4vcRsSKn7wc2ABMGOa6ahsdhXZ3yPlwCHJJ9OQe4KCIej4g/AD3ZXtdijYhrSsfmDRS/S+qWVvq2mUOBZRGxKSIeBJYBswcpzoaGXSJoV566vgy4sVT8wTxdXNRsOGGINfrTGpOBXYCHIuLJuvLBMjEi1ub0OmBiP/XnAhfWlZ2WffslSdt3PMIttRrvDpKWS7qhNozF0PcttNm/kg6g+PZ4T6l4MPu32XHYsE723WaKvmxl3U5qd3vzgZ+U5hsdE4Op1Xjfkq/vJZJqP64d6r7dSlf+xISknwHPb7Do0xFxWRvt7Ah8H/hIRPwpi88CTgEin78AvHs4xDsU+oq1PBMRIanpvcOSJgEvofjNR82JFB9wYyjuh/4kcPIwiHePiFgjaS/gakm/pfgA67gO9+/5wLyIeCqLO96/VSDpncBM4LWl4q2OiYi4p3ELQ+ZHwIUR8bik91GceR3c5ZiALiWCiHjDQNuQtB1FErggIn5Qant9qc43gR8PdFsdiLfZn9bYCIyVNDq/fQ34T270Fauk9ZImRcTa/CDa0EdTRwOXRsQTpbZr33Yfl/Rt4OMDibVT8UbEmny+V9K1FGeI36fDfdupeCU9D7ic4ovEDaW2O96/dVr5Ey+1OqsljQZ2pjhOh/rPw7S0PUlvoEjCr42Ix2vlTY6JwUwE/cYbERtLs9+iuKZUW/d1dete2/EI+zAih4ZyzPIc4K6I+GLdskml2f8O3D6UsTXR8E9rRHGl6BqKsXiAecBgnmEszW20sq23UzcsVOvb7P+jGPy+7TdeSeNqQyiSdgVeBdzZhb5tNd4xwKXAeRFxSd2ywe7fVv7ES3kf3gpcnX25FJibdxXtCUwHbupwfG3FKullwDeAIyNiQ6m84TExiLG2Gm/5s+lIimubUJx1z8q4xwGz2PJMfPAN5ZXpVh4UH96rgceB9cCVWb4bcEVOv5pi6Oc24NZ8HJ7Lzgd+m8uWApO6HW/OH05xd9M9FN8Ea+V7UbyheoDvAdsPYqy7AFcBK4CfAeOzfFq0EqwAAACqSURBVCbwrVK9aRTfUp5Vt/7V2be3A98Bdhzkvu03XuC/ZUy/yef53ejbNuJ9J/BE6bi9FdhvqPq30XFIMfx0ZE7vkH3Vk323V2ndT+d6dwOHDWZfthjrz/I9V+vHpf0dE12O9/PAHRnXNcA+pXXfnX3eAxw3FPGWH/4TE2ZmFTcih4bMzKxznAjMzCrOicDMrOKcCMzMKs6JwMys4pwIzMwqzonAzKzi/j+s9U5zApHQiwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L7emwfEi7eo_"
      },
      "source": [
        "### Low learning rate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XDRXpRl87f2p"
      },
      "source": [
        "def get_model():\n",
        "    model = nn.Sequential(\n",
        "        nn.Linear(28 * 28, 1000),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(1000, 10)\n",
        "    ).to(device)\n",
        "\n",
        "    loss_fn = nn.CrossEntropyLoss()\n",
        "    optimizer = Adam(model.parameters(), lr=1e-5)\n",
        "    return model, loss_fn, optimizer "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qZojB4U47iuN"
      },
      "source": [
        "trn_dl, val_dl = get_data()\n",
        "model, loss_fn, optimizer = get_model()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uJ12zMlZ7kt7",
        "outputId": "6d4ef703-341e-4b7b-fc2f-6765daf7c5c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "train_losses, train_accuracies = [], []\n",
        "val_losses, val_accuracies = [], []\n",
        "for epoch in range(5):\n",
        "    print(epoch)\n",
        "    train_epoch_losses, train_epoch_accuracies = [], []\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        batch_loss = train_batch(x, y, model, optimizer, loss_fn)\n",
        "        train_epoch_losses.append(batch_loss) \n",
        "    train_epoch_loss = np.array(train_epoch_losses).mean()\n",
        "\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        is_correct = accuracy(x, y, model)\n",
        "        train_epoch_accuracies.extend(is_correct)\n",
        "    train_epoch_accuracy = np.mean(train_epoch_accuracies)\n",
        "    for ix, batch in enumerate(iter(val_dl)):\n",
        "        x, y = batch\n",
        "        val_is_correct = accuracy(x, y, model)\n",
        "        validation_loss = val_loss(x, y, model)\n",
        "    val_epoch_accuracy = np.mean(val_is_correct)\n",
        "    train_losses.append(train_epoch_loss)\n",
        "    train_accuracies.append(train_epoch_accuracy)\n",
        "    val_losses.append(validation_loss)\n",
        "    val_accuracies.append(val_epoch_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-3bQsBA37mn_",
        "outputId": "acecb0df-38e0-47a5-bd6a-41de268ac3ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "source": [
        "epochs = np.arange(5)+1\n",
        "import matplotlib.ticker as mtick\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as mticker\n",
        "%matplotlib inline\n",
        "plt.subplot(211)\n",
        "plt.plot(epochs, train_losses, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_losses, 'r', label='Validation loss')\n",
        "plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation loss with 0.00001 learning rate')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()\n",
        "plt.subplot(212)\n",
        "plt.plot(epochs, train_accuracies, 'bo', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracies, 'r', label='Validation accuracy')\n",
        "plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation accuracy with 0.00001 learning rate')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.gca().set_yticklabels(['{:.0f}%'.format(x*100) for x in plt.gca().get_yticks()]) \n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2Gnn6Gq70BN",
        "outputId": "d4f3a595-5815-4a0e-9602-b0c2417a89f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for ix, par in enumerate(model.parameters()):\n",
        "    if(ix==0):\n",
        "        plt.hist(par.cpu().detach().numpy().flatten())\n",
        "        plt.title('Distribution of weights conencting input to hidden layer')\n",
        "        plt.show()\n",
        "    elif(ix ==1):\n",
        "        plt.hist(par.cpu().detach().numpy().flatten())\n",
        "        plt.title('Distribution of biases of hidden layer')\n",
        "        plt.show()\n",
        "    elif(ix==2):\n",
        "        plt.hist(par.cpu().detach().numpy().flatten())\n",
        "        plt.title('Distribution of weights conencting hidden to output layer')\n",
        "        plt.show()\n",
        "    elif(ix ==3):\n",
        "        plt.hist(par.cpu().detach().numpy().flatten())\n",
        "        plt.title('Distribution of biases of output layer')\n",
        "        plt.show() "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1COLvsAL77V9"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}